Molecular Subsets in Renal Cancer Determine Outcome to Checkpoint and Angiogenesis Blockade

Banana-shaped survival curves of metastatic renal cell carcinoma treated with first-line immune-combinations, not just a matter of "palateau"

The first-line therapy of metastatic renal cell carcinoma (mRCC) has revolutionized with the approval of immune checkpoint inhibitors (ICIs) in combination with or without tyrosine kinase inhibitors (TKIs). The choice among the many different immuno-combinations (ICI-ICI or ICI-TKI) is challenging due to the lack of predictive factors. The different shapes of the Kaplan-Meier survival curves (e.g. "banana-shaped curves") have raised many questions on the long-term survival benefit. Here, we analyzed the factors that could have impacted the different long-term survival, including the prognostic factors distribution (IMDC score), histological factors (sarcomatoid features, PD-L1 expression), and treatment characteristics (mechanism of action, duration, discontinuation rate). This overview highlights the factors that should be considered in the first-line setting for the patients' therapeutic choice and prognostic assessment. They are also fundamental parameters to examined for head-to-head studies and real-life, large-scale studies.

MOICS, a novel classier deciphering immune heterogeneity and aid precise management of clear cell renal cell carcinoma at multiomics level

Recent studies have indicated that the tumor immune microenvironment plays a pivotal role in the initiation and progression of clear cell renal cell carcinoma (ccRCC). However, the characteristics and heterogeneity of tumor immunity in ccRCC, particularly at the multiomics level, remain poorly understood. We analyzed immune multiomics datasets to perform a consensus cluster analysis and validate the clustering results across multiple internal and external ccRCC datasets; and identified two distinctive immune phenotypes of ccRCC, which we named multiomics immune-based cancer subtype 1 (MOICS1) and subtype 2 (MOICS2). The former, MOICS1, is characterized by an immune-hot phenotype with poor clinical outcomes, marked by significant proliferation of CD4+ and CD8+ T cells, fibroblasts, and high levels of immune inhibitory signatures; the latter, MOICS2, exhibits an immune-cold phenotype with favorable clinical characteristics, characterized by robust immune activity and high infiltration of endothelial cells and immune stimulatory signatures. Besides, a significant negative correlation between immune infiltration and angiogenesis were identified. We further explored the mechanisms underlying these differences, revealing that negatively regulated endopeptidase activity, activated cornification, and neutrophil degranulation may promote an immune-deficient phenotype, whereas enhanced monocyte recruitment could ameliorate this deficiency. Additionally, significant differences were observed in the genomic landscapes between the subtypes: MOICS1 exhibited mutations in TTN, BAP1, SETD2, MTOR, MUC16, CSMD3, and AKAP9, while MOICS2 was characterized by notable alterations in the TGF-β pathway. Overall, our work demonstrates that multi-immune omics remodeling analysis enhances the understanding of the immune heterogeneity in ccRCC and supports precise patient management.

Impact of body composition on outcomes of immune checkpoint inhibitor combination therapy in patients with previously untreated advanced renal cell carcinoma

BACKGROUND

Data on the association between body composition and outcomes in patients with advanced renal cell carcinoma (RCC) treated with immune checkpoint inhibitor (ICI) combination therapy are limited.

METHODS

We retrospectively evaluated the clinical and radiographic data of 159 patients with advanced RCC, including 84 receiving ICI dual combination therapy (immunotherapy [IO]-IO group) and 75 receiving combinations of ICIs with tyrosine kinase inhibitors (TKIs) (IO-TKI group). Pretreatment computed tomography images were used to calculate body composition, including skeletal muscle mass and fat tissue area. Sarcopenia was defined based on skeletal muscle and psoas muscle indexes. The total fat index, subcutaneous fat index (SFI), and visceral fat index were also calculated.

RESULTS

In the IO-IO treatment group, there was no significant association between body composition and survival or tumor response (P > 0.05). In the IO-TKI treatment group, the high SFI was associated with longer progression-free survival (hazard ratio, 2.70; P = 0.0091) and overall survival (hazard ratio, 26.0; P = 0.0246) than the low SFI, which remained significant after adjusting for covariates. Furthermore, in the high-SFI population, patients treated with IO-TKI therapy had longer progression-free survival (P = 0.0019) and overall survival (P = 0.0287) than those treated with IO-IO therapy, while there was no significant survival difference between the 2 treatment groups in the low-SFI population (P > 0.05).

CONCLUSION

The SFI can be potentially utilized as an effective predictive and prognostic biomarker for first-line ICI combination therapy for advanced RCC.

Deciphering glutamine metabolism patterns for malignancy and tumor microenvironment in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer characterized by metabolic reprogramming. Glutamine metabolism is pivotal in metabolic reprogramming, contributing to the significant heterogeneity observed in ccRCC. Consequently, developing prognostic markers associated with glutamine metabolism could enhance personalized treatment strategies for ccRCC patients. This study obtained RNA sequencing and clinical data from 763 ccRCC cases sourced from multiple databases. Consensus clustering of 74 glutamine metabolism related genes (GMRGs)- profiles stratified the patients into three clusters, each of which exhibited distinct prognosis, tumor microenvironment, and biological characteristics. Then, six genes (SMTNL2, MIOX, TMEM27, SLC16A12, HRH2, and SAA1) were identified by machine-learning algorithms to develop a predictive signature related to glutamine metabolism, termed as GMRScore. The GMRScore showed significant differences in clinical prognosis, expression profile of immune checkpoints, abundance of immune cells, and immunotherapy response of ccRCC patients. Besides, the nomogram incorporating the GMRScore and clinical features showed strong predictive performance in prognosis of ccRCC patients. ALDH18A1, one of the GRMGs, exhibited elevated expression level in ccRCC and was related to markedly poorer prognosis in the integrated cohort, validated by proteomic profiling of 232 ccRCC samples from Fudan University Shanghai Cancer Center (FUSCC). Conducting western blotting, CCK-8, transwell, and flow cytometry assays, we found the knockdown of ALDH18A1 in ccRCC significantly promoted apoptosis and inhibited proliferation, invasion, and epithelial-mesenchymal transition (EMT) in two human ccRCC cell lines (786-O and 769-P). In conclusion, we developed a glutamine metabolism-related prognostic signature in ccRCC, which is tightly linked to the tumor immune microenvironment and immunotherapy response, potentially facilitating precision therapy for ccRCC patients. Additionally, this study revealed the key role of ALDH18A1 in promoting ccRCC progression for the first time.

Immune phenotype-genotype associations in primary clear cell renal cell carcinoma and matched metastatic tissue

Adjuvant immunotherapy has been recently recommended for patients with metastatic ccRCC, but there are no tissue biomarkers to predict treatment response in ccRCC. Potential predictive biomarkers are mainly assessed in primary tumor tissue, whereas metastases remain understudied. To explore potential differences between genomic alterations and immune phenotypes in primary tumors and their matched metastases, we analyzed primary tumors (PTs) of 47 ccRCC patients and their matched distant metastases (METs) by comprehensive targeted parallel sequencing, whole-genome copy number variation (CNV) analysis, determination of microsatellite instability (MSI) and tumor mutational burden (TMB). We quantified the spatial distribution of tumor-infiltrating CD8+ T cells, and co-expression of the T-cell-exhaustion marker TOX by digital immunoprofiling and quantified tertiary lymphoid structures (TLS). Most METs were pathologically "cold". Inflamed, pathologically "hot" PTs were associated with a decreased disease-free survival (DFS), worst for patients with high levels of CD8+TOX+ T cells. Interestingly, inflamed METs showed a relative increase of exhausted CD8+TOX+ T cells and increased accumulative size of TLS compared to PTs. Integrative analysis of molecular and immune phenotypes revealed BAP1 and CDKN2A/B deficiency to be associated with an inflamed immune phenotype. Our results highlight the distinct spatial distribution and differentiation of CD8+ T cells at metastatic sites, and the association of an inflamed microenvironment with specific genomic alterations.

Preoperative neoadjuvant targeted therapy remodels intra-tumoral heterogeneity of clear-cell renal cell carcinoma and ferroptosis inhibition induces resistance progression

Neoadjuvant tyrosine kinase inhibitor (TKI) therapy is an important treatment option for advanced renal cell carcinoma (RCC). Many RCC patients may fail to respond or be resistant to TKI therapy. We aimed to explore the key mechanisms of neoadjuvant therapy résistance. We obtained tumor samples from matched pre-treatment biopsy and post-treatment surgical samples and performed single-cell RNA sequencing. Sunitinib-resistant ccRCC cell lines were established. Ferroptosis was detected by ferrous ion and lipid peroxidation levels. Tumor growth and resistance to Sunitinib was validated in vitro and vivo. Immunohistochemistry was used to validate the levels key genes and lipid peroxidation. Multi-center cohorts were included, including TCGA, ICGC, Checkmate-025 and IMmotion151 clinical trial. Survival analysis was performed to identify the associated clinical and genomic variables. Intratumoral heterogeneity was first described in the whole neoadjuvant management. The signature of endothelial cells was correlated with drug sensitivity and progression-free survival. Ferroptosis was shown to be the key biological program in malignant cell resistance. We observed tissue lipid peroxidation was negatively correlated with IL6 and tumor response. TKI-resistant cell line was established. SLC7A11 knockdown promoted cell growth and lipid peroxidation, increased the ferroptosis level, and suppressed the growth of tumor xenografts significantly (P < 0.01). IL6 could reverse the ferroptosis and malignant behavior caused by SLC7A11 (-) via JAK2/STAT3 pathway, which was rescued by the ferroptosis inducer Erastin. Our data indicate that ferroptosis is a novel strategy for advanced RCC treatment, which activated by IL6, providing a new idea for resistance to TKIs.

TimiGP-Response: the pan-cancer immune landscape associated with response to immunotherapy

Accumulating evidence suggests that the tumor immune microenvironment (TIME) significantly influences the response to immunotherapy, yet this complex relationship remains elusive. To address this issue, we developed TimiGP-Response (TIME Illustration based on Gene Pairing designed for immunotherapy Response), a computational framework leveraging single-cell and bulk transcriptomic data, along with response information, to construct cell-cell interaction networks associated with responders and estimate the role of immune cells in treatment response. This framework was showcased in triple-negative breast cancer treated with immune checkpoint inhibitors targeting the PD-1:PD-L1 interaction, and orthogonally validated with imaging mass cytometry. As a result, we identified CD8+ GZMB+ T cells associated with responders and its interaction with regulatory T cells emerged as a potential feature for selecting patients who may benefit from these therapies. Subsequently, we analyzed 3,410 patients with seven cancer types (melanoma, non-small cell lung cancer, renal cell carcinoma, metastatic urothelial carcinoma, hepatocellular carcinoma, breast cancer, and esophageal cancer) treated with various immunotherapies and combination therapies, as well as several chemo- and targeted therapies as controls. Using TimiGP-Response, we depicted the pan-cancer immune landscape associated with immunotherapy response at different resolutions. At the TIME level, CD8 T cells and CD4 memory T cells were associated with responders, while anti-inflammatory (M2) macrophages and mast cells were linked to non-responders across most cancer types and datasets. Given that T cells are the primary targets of these immunotherapies and our TIME analysis highlights their importance in response to treatment, we portrayed the pan-caner landscape on 40 T cell subtypes. Notably, CD8+ and CD4+ GZMK+ effector memory T cells emerged as crucial across all cancer types and treatments, while IL-17-producing CD8+ T cells were top candidates associated with immunotherapy non-responders. In summary, this study provides a computational method to study the association between TIME and response across the pan-cancer immune landscape, offering resources and insights into immune cell interactions and their impact on treatment efficacy.

Patients with ASPSCR1-TFE3 fusion achieve better response to ICI based combination therapy among TFE3-rearranged renal cell carcinoma

BACKGROUND

TFE3-rearranged renal cell carcinoma (TFE3-rRCC) is a rare but highly heterogeneous renal cell carcinoma (RCC) entity, of which the clinical treatment landscape is largely undefined. This study aims to evaluate and compare the efficacy of different systemic treatments and further explore the molecular correlates.

METHODS

Thirty-eight patients with metastatic TFE3-rRCC were enrolled. Main outcomes included progression-free survival (PFS), overall survival, objective response rate (ORR) and disease control rate. RNA sequencing was performed on 32 tumors.

RESULTS

Patients receiving first-line immune checkpoint inhibitor (ICI) based combination therapy achieved longer PFS than those treated without ICI (median PFS: 11.5 vs. 5.1 months, P = 0.098). After stratification of fusion partners, the superior efficacy of first-line ICI based combination therapy was predominantly observed in ASPSCR1-TFE3 rRCC (median PFS: not reached vs. 6.5 months, P = 0.01; ORR: 67.5% vs. 10.0%, P = 0.019), but almost not in non-ASPSCR1-TFE3 rRCC. Transcriptomic data revealed enrichment of ECM and collagen-related signaling in ASPSCR1-TFE3 rRCC, which might interfere with the potential efficacy of anti-angiogenic monotherapy. Whereas angiogenesis and immune activities were exclusively enriched in ASPSCR1-TFE3 rRCC and promised the better clinical outcomes with ICI plus tyrosine kinase inhibitor combination therapy.

CONCLUSIONS

The current study represents the largest cohort comparing treatment outcomes and investigating molecular correlates of metastatic TFE3-rRCC based on fusion partner stratification. ICI based combination therapy could serve as an effective first-line treatment option for metastatic ASPSCR1-TFE3 rRCC patients. Regarding with other fusion subtypes, further investigations should be performed to explore the molecular mechanisms to propose pointed therapeutic strategy accordingly.

Epigenomic signatures of sarcomatoid differentiation to guide the treatment of renal cell carcinoma

Renal cell carcinoma with sarcomatoid differentiation (sRCC) is associated with poor survival and a heightened response to immune checkpoint inhibitors (ICIs). Two major barriers to improving outcomes for sRCC are the limited understanding of its gene regulatory programs and the low diagnostic yield of tumor biopsies due to spatial heterogeneity. Herein, we characterized the epigenomic landscape of sRCC by profiling 107 epigenomic libraries from tissue and plasma samples from 50 patients with RCC and healthy volunteers. By profiling histone modifications and DNA methylation, we identified highly recurrent epigenomic reprogramming enriched in sRCC. Furthermore, CRISPRa experiments implicated the transcription factor FOSL1 in activating sRCC-associated gene regulatory programs, and FOSL1 expression was associated with the response to ICIs in RCC in two randomized clinical trials. Finally, we established a blood-based diagnostic approach using detectable sRCC epigenomic signatures in patient plasma, providing a framework for discovering epigenomic correlates of tumor histology via liquid biopsy.

Multidisciplinary systemic and local therapies for metastatic renal cell carcinoma: a narrative review

INTRODUCTION

Systemic and local therapies for patients with metastatic renal cell carcinoma (mRCC) are often challenging despite the evolution of multimodal cancer therapies in the last decade. In this review, we will focus on recent multidisciplinary approaches for patients with mRCC.

AREAS COVERED

Systemic therapies for patients with mRCC have been garnering attention particularly after the approval of immuno-oncology (IO) agents, including anti-programmed death 1/programmed death-ligand 1. IO combinations have significantly prolonged overall survival in patients with mRCC in the first-line setting. Regarding local therapies, cytoreductive nephrectomy (CN) has become less common in the post-Cancer du Rein Metastatique Nephrectomie et Antiangiogéniques (CARMENA) trial era, even though CN may still benefit selected patients with mRCC. In addition, metastasis-directed local therapies, namely metastasectomy or stereotactic radiotherapy, particularly for oligo-metastatic lesions or brain metastases, may have a prognostic impact. Several ablative techniques are also evolving while maintaining high local control rates with acceptable safety.

EXPERT OPINION

Multimodal cancer therapies are essential for conquering complex cases of mRCC. Modern systemic therapies including IO-based combination therapy as well as local therapies including CN, metastasectomy, stereotactic radiotherapy, and ablative techniques appear to improve oncologic outcomes of patients with mRCC, although appropriate patient selection is indispensable.

Validation of the Lung Immune Prognostic Index (LIPI) as a prognostic biomarker in metastatic renal cell carcinoma

BACKGROUND

The Lung Immune Prognostic Index (LIPI) is associated with immune checkpoint inhibitors (ICI) outcomes across different solid tumors, particularly in non-small cell lung cancer. Data regarding the prognostic and/or predictive role of LIPI in metastatic renal cell carcinoma (mRCC) are still scarce. The aim of this study was to evaluate whether LIPI could be predictive of survival in mRCC patients.

METHODS

We used patient level data from three different prospective studies (NIVOREN trial: nivolumab; TORAVA trial: VEGF/VEGFR-targeted therapy (TT); CheckMate 214: nivolumab-ipilimumab vs sunitinib). LIPI was calculated based on a derived neutrophils/(leukocyte-neutrophil) ratio > 3 and lactate-dehydrogenase >upper limit of normal, classifying patients into three groups (LIPI good, 0 factors;LIPI intermediate (int), 1 factor;LIPI poor, 2 factors) and/or into two groups (LIPI good, 0 factors;LIPI int/poor, 1-2 factors) according to trial sample size. Primary and secondary endpoints were overall survival (OS) and progression-free survival (PFS).

RESULTS

In the Nivolumab dataset (n = 619), LIPI was significantly associated with OS (LIPI-good 30.1 vs 13.8 months in the LIPI int/poor; HR= 0.47) and PFS (HR=0.74). In the VEGF/VEGFR-TT dataset (n = 159), only a correlation with PFS was observed. In the CheckMate214 dataset (n = 1084), LIPI was significantly associated with OS (nivolumab-ipilimumab OS LIPI good vs int/poor: HR=0.55, p < 0.0001; sunitinib: OS LIPI good vs int/poor: 0.38, p < 0.0001) in both treatment groups in univariate and multivariate analysis.

CONCLUSIONS

Pretreatment-LIPI correlated with worse survival outcomes in mRCC treated with either ICI or antiangiogenic therapy, confirming LIPI's prognostic role in mRCC irrespective of systemic treatment used.

Renal Cell Carcinoma of Variant Histology: New Biologic Understanding Leads to Therapeutic Advances

Renal cell carcinoma (RCC) is one of the 10 most commonly diagnosed solid tumors. Most RCCs are histologically defined as clear cell, comprising approximately 75% of diagnoses. However, the remaining RCC cases are composed of a heterogeneous combination of diverse histopathologic subtypes, each with unique pathogeneses and clinical features. Although the therapeutic approach to both localized and metastatic RCCs has dramatically changed, first with the advent of antiangiogenic targeted therapies and more recently with the approval of immune checkpoint inhibitor (ICI)-based combinations, these advances have primarily benefited the clear cell RCC patient population. As such, there remains critical gaps in the optimization of treatment regimens for patients with non-clear cell, or variant, RCC histologies. Herein, we detail recent advances in understanding the biology of RCC with variant histology and how such findings have guided novel clinical studies investigating precision oncology approaches for these rare subtypes. Among the most common variant histology RCCs are papillary RCC, comprising approximately 15%-20% of all diagnoses. Although a histopathologically diverse subset of tumors, papillary RCC is canonically associated with amplification of the MET protooncogene; recently completed and ongoing trials have investigated MET-directed therapies for this patient population. Finally, we discuss the unique biology of RCC with sarcomatoid dedifferentiation and the recent clinical findings detailing its paradoxical sensitivity to ICIs.

A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma

An important challenge in the real-world management of patients with advanced clear-cell renal cell carcinoma (aRCC) is determining who might benefit from immune checkpoint blockade (ICB). Here we performed a comprehensive multiomics mapping of aRCC in the context of ICB treatment, involving discovery analyses in a real-world data cohort followed by validation in independent cohorts. We cross-connected bulk-tumor transcriptomes across >1,000 patients with validations at single-cell and spatial resolutions, revealing a patient-specific crosstalk between proinflammatory tumor-associated macrophages and (pre-)exhausted CD8+ T cells that was distinguished by a human leukocyte antigen repertoire with higher preference for tumoral neoantigens. A cross-omics machine learning pipeline helped derive a new tumor transcriptomic footprint of neoantigen-favoring human leukocyte antigen alleles. This machine learning signature correlated with positive outcome following ICB treatment in both real-world data and independent clinical cohorts. In experiments using the RENCA-tumor mouse model, CD40 agonism combined with PD1 blockade potentiated both proinflammatory tumor-associated macrophages and CD8+ T cells, thereby achieving maximal antitumor efficacy relative to other tested regimens. Thus, we present a new multiomics and spatial map of the immune-community architecture that drives ICB response in patients with aRCC.

Neopterin as a potential prognostic and predictive biomarker in metastatic renal cell carcinoma treated with immune checkpoint inhibitors

INTRODUCTION

Immunotherapy represents a significant and essential component of renal carcinoma therapy (RCC), but the selection of an optimal regimen for an individual patient remains unclear. Despite significant improvements in therapeutic options for RCC, predictive biomarkers for immunotherapeutic agents remain elusive. Neopterin is a biomarker of cell-mediated immune response, with concentrations increased in different disorders, including cancer. High neopterin levels herald, in general, a poor prognosis.

AREAS COVERED

This review briefly overviews the contemporary clinical data on biomarkers in metastatic RCC therapy, focusing on neopterin.

EXPERT OPINION

Elevated neopterin levels have been observed in tumors of different primary locations. Research indicates that neopterin may serve as a potential biomarker for assessing the inflammatory status associated with certain cancers. However, it is necessary to interpret neopterin levels in the context of a comprehensive clinical evaluation, as elevated neopterin alone is not specific to cancer and can be influenced by other factors, including comorbid conditions. Neopterin has also been identified as a prognostic biomarker. An increasing neopterin level in serum and urine is associated with advanced cancer, but the role as a potential predictor of response to immunotherapy has yet to be established. A reliable biomarker for optimal therapy selection in metastatic RCC is still putative.

Biomarqueurs émergents dans les cancers du rein à cellules claires

Emerging biomarkers in clear cell kidney cancer.

The Evolving Landscape of Biomarkers for Immune Checkpoint Blockade in Genitourinary Cancers

In the past decade, immune checkpoint inhibitors (ICI) have been approved for treatment of genitourinary malignancies and have revolutionized the treatment landscape of these tumors. However, despite the remarkable success of these therapies in some GU malignancies, many patients' tumors do not respond to these therapies, and others may experience significant side effects, such as immune-related adverse events (iRAEs). Accordingly, biomarkers and improved prognostic tools are critically needed to help predict which patients will respond to ICI, predict and mitigate risk of developing immune-related adverse events, and inform personalized choice of therapy for each patient. Ongoing clinical and preclinical studies continue to provide an increasingly robust understanding of the mechanisms of the response to immunotherapy, which continue to inform biomarker development and validation. Herein, we provide a comprehensive review of biomarkers of the response to immunotherapy in GU tumors and their role in selection of therapy and disease monitoring.

Molecular analysis of primary and metastatic sites in patients with renal cell carcinoma

BACKGROUNDMetastases are the hallmark of lethal cancer, though underlying mechanisms that drive metastatic spread to specific organs remain poorly understood. Renal cell carcinoma (RCC) is known to have distinct sites of metastases, with lung, bone, liver, and lymph nodes being more common than brain, gastrointestinal tract, and endocrine glands. Previous studies have shown varying clinical behavior and prognosis associated with the site of metastatic spread; however, little is known about the molecular underpinnings that contribute to the differential outcomes observed by the site of metastasis.METHODSWe analyzed primary renal tumors and tumors derived from metastatic sites to comprehensively characterize genomic and transcriptomic features of tumor cells as well as to evaluate the tumor microenvironment at both sites.RESULTSWe included a total of 657 tumor samples (340 from the primary site [kidney] and 317 from various sites of metastasis). We show distinct genomic alterations, transcriptomic signatures, and immune and stromal tumor microenvironments across metastatic sites in a large cohort of patients with RCC.CONCLUSIONWe demonstrate significant heterogeneity among primary tumors and metastatic sites and elucidate the complex interplay between tumor cells and the extrinsic tumor microenvironment that is vital for developing effective anticancer therapies.

Histopathology Based AI Model Predicts Anti-Angiogenic Therapy Response in Renal Cancer Clinical Trial

Background

Predictive biomarkers of treatment response are lacking for metastatic clearcell renal cell carcinoma (ccRCC), a tumor type that is treated with angiogenesis inhibitors, immune checkpoint inhibitors, mTOR inhibitors and a HIF2 inhibitor. The Angioscore, an RNA-based quantification of angiogenesis, is arguably the best candidate to predict anti-angiogenic (AA) response. However, the clinical adoption of transcriptomic assays faces several challenges including standardization, time delay, and high cost. Further, ccRCC tumors are highly heterogenous, and sampling multiple areas for sequencing is impractical.

Approach

Here we present a novel deep learning (DL) approach to predict the Angioscore from ubiquitous histopathology slides. In order to overcome the lack of interpretability, one of the biggest limitations of typical DL models, our model produces a visual vascular network which is the basis of the model's prediction. To test its reliability, we applied this model to multiple cohorts including a clinical trial dataset.

Results

Our model accurately predicts the RNA-based Angioscore on multiple independent cohorts (spearman correlations of 0.77 and 0.73). Further, the predictions help unravel meaningful biology such as association of angiogenesis with grade, stage, and driver mutation status. Finally, we find our model is able to predict response to AA therapy, in both a real-world cohort and the IMmotion150 clinical trial. The predictive power of our model vastly exceeds that of CD31, a marker of vasculature, and nearly rivals the performance (c-index 0.66 vs 0.67) of the ground truth RNA-based Angioscore at a fraction of the cost.

Conclusion

By providing a robust yet interpretable prediction of the Angioscore from histopathology slides alone, our approach offers insights into angiogenesis biology and AA treatment response.

UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples

Comprehensively studying metabolism requires the measurement of metabolite levels. However, in contrast to the broad availability of gene expression data, metabolites are rarely measured in large molecularly-defined cohorts of tissue samples. To address this basic barrier to metabolic discovery, we propose a Bayesian framework ("UnitedMet") which leverages the empirical strength of RNA-metabolite covariation to impute otherwise unmeasured metabolite levels from widely available transcriptomic data. We demonstrate that UnitedMet is equally capable of imputing whole pool sizes as well as the outcomes of isotope tracing experiments. We apply UnitedMet to investigate the metabolic impact of driver mutations in kidney cancer, identifying a novel association between BAP1 and a highly oxidative tumor phenotype. We similarly apply UnitedMet to determine that advanced kidney cancers upregulate oxidative phosphorylation relative to early-stage disease, that oxidative metabolism in kidney cancer is associated with inferior outcomes to combination therapy, and that kidney cancer metastases themselves demonstrate elevated oxidative phosphorylation relative to primary tumors. UnitedMet therefore enables the assessment of metabolic phenotypes in contexts where metabolite measurements were not taken or are otherwise infeasible, opening new avenues for the generation and evaluation of metabolite-centered hypotheses. UnitedMet is open source and publicly available (https://github.com/reznik-lab/UnitedMet).

Comprehensive proteogenomic characterization of rare kidney tumors

Non-clear cell renal cell carcinomas (non-ccRCCs) encompass diverse malignant and benign tumors. Refinement of differential diagnosis biomarkers, markers for early prognosis of aggressive disease, and therapeutic targets to complement immunotherapy are current clinical needs. Multi-omics analyses of 48 non-ccRCCs compared with 103 ccRCCs reveal proteogenomic, phosphorylation, glycosylation, and metabolic aberrations in RCC subtypes. RCCs with high genome instability display overexpression of IGF2BP3 and PYCR1. Integration of single-cell and bulk transcriptome data predicts diverse cell-of-origin and clarifies RCC subtype-specific proteogenomic signatures. Expression of biomarkers MAPRE3, ADGRF5, and GPNMB differentiates renal oncocytoma from chromophobe RCC, and PIGR and SOSTDC1 distinguish papillary RCC from MTSCC. This study expands our knowledge of proteogenomic signatures, biomarkers, and potential therapeutic targets in non-ccRCC.

Identification of novel snoRNA-based biomarkers for clear cell renal cell carcinoma from urine-derived extracellular vesicles

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC with high rates of metastasis. Targeted therapies such as tyrosine kinase and checkpoint inhibitors have improved treatment success, but therapy-related side effects and tumor recurrence remain a challenge. As a result, ccRCC still have a high mortality rate. Early detection before metastasis has great potential to improve outcomes, but no suitable biomarker specific for ccRCC is available so far. Therefore, molecular biomarkers derived from body fluids have been investigated over the past decade. Among them, RNAs from urine-derived extracellular vesicles (EVs) are very promising.

METHODS

RNA was extracted from urine-derived EVs from a cohort of 78 subjects (54 ccRCC patients, 24 urolithiasis controls). RNA-seq was performed on the discovery cohort, a subset of the whole cohort (47 ccRCC, 16 urolithiasis). Reads were then mapped to the genome, and expression was quantified based on 100 nt long contiguous genomic regions. Cluster analysis and differential region expression analysis were performed with adjustment for age and gender. The candidate biomarkers were validated by qPCR in the entire cohort. Receiver operating characteristic, area under the curve and odds ratios were used to evaluate the diagnostic potential of the models.

RESULTS

An initial cluster analysis of RNA-seq expression data showed separation by the subjects' gender, but not by tumor status. Therefore, the following analyses were done, adjusting for gender and age. The regions differentially expressed between ccRCC and urolithiasis patients mainly overlapped with small nucleolar RNAs (snoRNAs). The differential expression of four snoRNAs (SNORD99, SNORD22, SNORD26, SNORA50C) was validated by quantitative PCR. Confounder-adjusted regression models were then used to classify the validation cohort into ccRCC and tumor-free subjects. Corresponding accuracies ranged from 0.654 to 0.744. Models combining multiple genes and the risk factors obesity and hypertension showed improved diagnostic performance with an accuracy of up to 0.811 for SNORD99 and SNORA50C (p = 0.0091).

CONCLUSIONS

Our study uncovered four previously unrecognized snoRNA biomarkers from urine-derived EVs, advancing the search for a robust, easy-to-use ccRCC screening method.

Characterization of FOLH1 Expression in Renal Cell Carcinoma

PURPOSE

Given the emergence of PSMA-targeted diagnostic agents and therapeutics, we sought to investigate patterns of FOLH1 expression in RCC and their impacts on RCC outcomes.

METHODS

We conducted a pooled multi-institutional analysis of patients with RCC having undergone DNA and RNA next-generation sequencing. FOLH1-high/low expression was defined as the ≥75th/<25th percentile of RNA transcripts per million (TPM). Angiogenic, T-effector, and myeloid expression signatures were calculated using previously defined gene sets. Kaplan-Meier estimates were calculated from the time of tissue collection or therapy start.

RESULTS

We included 1,724 patients in the analysis. FOLH1 expression was significantly higher in clear cell (71%) compared to non-clear cell RCC tumors (19.0 versus 3.3 TPM, p < 0.001) and varied by specimen site (45% primary kidney/55% metastasis, 13.6 versus 9.9 TPM, p < 0.001). FOLH1 expression was correlated with angiogenic gene expression (Spearman = 0.76, p < 0.001) and endothelial cell abundance (Spearman = 0.76, p < 0.001). While OS was similar in patients with FOLH1-high versus -low ccRCC, patients with FOLH1-high clear cell tumors experienced a longer time on cabozantinib treatment (9.7 versus 4.6 months, respectively, HR 0.57, 95% CI 0.35-0.93, p < 0.05).

CONCLUSIONS

We observed differential patterns of FOLH1 expression based on histology and tumor site in RCC. FOLH1 was correlated with angiogenic gene expression, increased OS, and a longer duration of cabozantinib treatment.

Molecular subtypes as potential biomarkers in renal cell carcinoma

The increasing number of approved treatment combinations for metastatic renal cell carcinoma highlights the need for actionable biomarkers to guide treatment selection. In this issue of Cancer Cell, Saliby et al. validate the distinct clinico-genomic profiles of expression-based molecular clusters identified in the IMmotion151 trial in the JAVELIN Renal 101 dataset.

Impact of renal cell carcinoma molecular subtypes on immunotherapy and targeted therapy outcomes

Saliby et al. show that a machine learning approach can accurately classify clear cell renal cell carcinoma (RCC) into distinct molecular subtypes using transcriptomic data. When applied to tumors biospecimens from the JAVELIN Renal 101 (JR101) trial, a benefit is observed with immune checkpoint inhibitor (ICI)-based therapy across all molecular subtypes.

Differential expression analysis identifies a prognostically significant extracellular matrix-enriched gene signature in hyaluronan-positive clear cell renal cell carcinoma

Hyaluronan (HA) accumulation in clear cell renal cell carcinoma (ccRCC) is associated with poor prognosis; however, its biology and role in tumorigenesis are unknown. RNA sequencing of 48 HA-positive and 48 HA-negative formalin-fixed paraffin-embedded (FFPE) samples was performed to identify differentially expressed genes (DEG). The DEGs were subjected to pathway and gene enrichment analyses. The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) data and DEGs were used for the cluster analysis. In total, 129 DEGs were identified. HA-positive tumors exhibited enhanced expression of genes related to extracellular matrix (ECM) organization and ECM receptor interaction pathways. Gene set enrichment analysis showed that epithelial-mesenchymal transition-associated genes were highly enriched in the HA-positive phenotype. A protein-protein interaction network was constructed, and 17 hub genes were discovered. Heatmap analysis of TCGA-KIRC data identified two prognostic clusters corresponding to HA-positive and HA-negative phenotypes. These clusters were used to verify the expression levels and conduct survival analysis of the hub genes, 11 of which were linked to poor prognosis. These findings enhance our understanding of hyaluronan in ccRCC.

Molecular mechanisms and clinical relevance of endothelial cell cross-talk in clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer in adults and stands out as one of the most vascularized and immune-infiltrated solid tumors. Overproduction of vascular endothelial growth factor A promotes uncontrolled growth of abnormal vessels and immunosuppression, and the tumor microenvironment (TME) has a prominent role in disease progression, drug targeting and drug response, and for patient outcome.

Methods

Studies of experimental models, large-scale omics approaches, and patient prognosis and therapy prediction, using gene expression signatures and tissue biomarker analysis, have been reviewed for enhanced understanding of the endothelium in ccRCC and the interplay with the surrounding TME.

Results

Preclinical and clinical studies have discovered molecular mechanisms of endothelial cross-talk of relevance for disease progression, patient prognosis, and therapy prediction. There is, however, a lack of representative ccRCC experimental models. Omics approaches have identified clinically relevant subsets of angiogenic and immune-infiltrated tumors with distinct molecular signatures and distinct endothelial cell and immune cell populations in patients.

Conclusions

Recent genetically engineered ccRCC mouse models together with emerging evidence from single cell RNA sequencing data open up for future validation studies, including multiplex imaging of ccRCC patient cohorts. These studies are of importance for therapy benefit and personalized treatment of ccRCC patients.

Molecular mechanism of formation and destruction of a pseudo‑capsule in clear cell renal cell carcinoma

The process and molecular mechanisms underlying the formation and destruction of a pseudo-capsule (PC) in clear cell renal cell carcinoma (ccRCC) are poorly understood. In the present study, the PCs of surgical specimens from primary tumors and metastatic lesions in 169 patients with ccRCC, and carcinogen-induced ccRCC rat models were semi-quantified using the invasion of PC (i-Cap) score system. This was based on the relationship among the tumor, PC and adjacent normal tissue (NT) as follows: i-Cap 0, tumor has no PC and does not invade NT; i-Cap 1, tumor has a complete PC and does not invade into the PC; i-Cap 2, tumor with focal absences in the PC, which partially invades the PC but not completely through the PC; i-Cap 3, tumor crosses the PC and invades the NT; i-Cap 4, tumor directly invades the NT without a PC. The study suggested that PC formation was not observed without physical compression, and also revealed that tumor invasion into the PC was a prognostic factor for postoperative oncological outcomes. Higher i-Cap, Fuhrman grade and tumor size were independent poor prognostic factors for postoperative disease-free survival. mRNA expression arrays generated from carcinogen-induced ccRCC rat models were used to explore genes potentially associated with the formation and destruction of a PC. Subsequently, human ccRCC specimens were validated for four genes identified via expression array; the results revealed that collagen type 4A2, matrix metalloproteinase-7 and l-selectin were upregulated alongside the progression of i-Cap score. Conversely, endoglin was downregulated. In conclusion, the present study provides insights into the formation and destruction of a PC, and the results may aid the treatment and management of patients with ccRCC.

Renal cell carcinoma histologic subtypes exhibit distinct transcriptional profiles

Molecular profiling of clear cell renal cell carcinoma (ccRCC) tumors of patients in a clinical trial has identified distinct transcriptomic signatures with predictive value, yet data in non-clear cell variants (nccRCC) are lacking. We examined the transcriptional profiles of RCC tumors representing key molecular pathways, from a multi-institutional, real-world patient cohort, including ccRCC and centrally reviewed nccRCC samples. ccRCC had increased angiogenesis signature scores compared with the heterogeneous group of nccRCC tumors, while cell cycle, fatty acid oxidation/AMPK signaling, and fatty acid synthesis/pentose phosphate signature scores were increased in one or more nccRCC subtypes. Among both ccRCC and nccRCC tumors, T effector scores statistically correlated with increased immune cell infiltration and were more commonly associated with immunotherapy-related markers (PD-L1+/TMBhi/MSIhi). In conclusion, this study provides evidence of differential gene transcriptional profiles among ccRCC versus nccRCC tumors, providing insights for optimizing personalized and histology-specific therapeutic strategies for patients with advanced RCC.

Molecular underpinnings of dedifferentiation and aggressiveness in chromophobe renal cell carcinoma

Sarcomatoid dedifferentiation is common to multiple renal cell carcinoma (RCC) subtypes, including chromophobe RCC (ChRCC), and is associated with increased aggressiveness, resistance to targeted therapies, and heightened sensitivity to immunotherapy. To study ChRCC dedifferentiation, we performed multiregion integrated paired pathological and genomic analyses. Interestingly, ChRCC dedifferentiates not only into sarcomatoid but also into anaplastic and glandular subtypes, which are similarly associated with increased aggressiveness and metastases. Dedifferentiated ChRCC shows loss of epithelial markers, convergent gene expression, and whole genome duplication from a hypodiploid state characteristic of classic ChRCC. We identified an intermediate state with atypia and increased mitosis but preserved epithelial markers. Our data suggest that dedifferentiation is initiated by hemizygous mutation of TP53, which can be observed in differentiated areas, as well as mutation of PTEN. Notably, these mutations become homozygous with duplication of preexisting monosomes (i.e., chromosomes 17 and 10), which characterizes the transition to dedifferentiated ChRCC. Serving as potential biomarkers, dedifferentiated areas become accentuated by mTORC1 activation (phospho-S6) and p53 stabilization. Notably, dedifferentiated ChRCC share gene enrichment and pathway activation features with other sarcomatoid RCC, suggesting convergent evolutionary trajectories. This study expands our understanding of aggressive ChRCC, provides insight into molecular mechanisms of tumor progression, and informs pathologic classification and diagnostics.

Tumor immune dysfunction and exclusion subtypes in bladder cancer and pan-cancer: a novel molecular subtyping strategy and immunotherapeutic prediction model

BACKGROUND

Molecular subtyping is expected to enable precise treatment. However, reliable subtyping strategies for clinical application remains defective and controversial. Given the significance of tumor immune dysfunction and exclusion (TIDE), we aimed to develop a novel TIDE-based subtyping strategy to guide personalized immunotherapy in the bladder cancer (BC).

METHODS

Transcriptome data of BC was used to evaluate the heterogeneity and the status of TIDE patterns. Subsequently, consensus clustering was applied to classify BC patients based on TIDE marker-genes. Patients' clinicopathological, molecular features and signaling pathways of the different TIDE subtypes were well characterized. We also utilize the deconvolution algorithms to analyze the tumor microenvironment, and further explore the sensitivity and mechanisms of each subtype to immunotherapy. Furthermore, BC patient clinical information, real-world BC samples and urine samples were collected for the validation of our findings, which were used for RNA-seq analysis, H&E staining, immunohistochemistry and immunofluorescence staining, and enzyme-linked immunosorbent assay. Finally, we also explored the conservation of our novel TIDE subtypes in pan-cancers.

RESULTS

We identified 69 TIDE biomarker genes and classified BC samples into three subtypes using consensus clustering. Subtype I showed the lowest TIDE status and malignancy with the best prognosis and highest sensitivity to immune checkpoint blockade (ICB) treatment, which was enriched of metabolic related signaling pathways. Subtype III represented the highest TIDE status and malignancy with the poorest prognosis and resistance to ICB treatment, resulting from its inhibitory immune microenvironment and T cell terminal exhaustion. Subtype II was in a transitional state with intermediate TIDE level, malignancy, and prognosis. We further confirmed the existence and characteristics of our novel TIDE subtypes using real-world BC samples and collected patient clinical data. This subtyping method was proved to be more efficient than previous known methods in identifying non-responders to immunotherapy. We also propose that combining our TIDE subtypes with known biomarkers can potentially improve the sensitivity and specificity of these biomarkers. Moreover, besides guiding ICB treatment, this classification approach can assist in selecting the frontline or recommended drugs. Finally, we confirmed that the TIDE subtypes are conserved across the pan-tumors.

CONCLUSIONS

Our novel TIDE-based subtyping method can serve as a powerful clinical tool for BC and pan-cancer patients, and potentially guiding personalized therapy decisions for selecting potential beneficiaries and excluding resistant patients of ICB therapy.

Rationale for immune checkpoint inhibitors plus targeted therapy for advanced renal cell carcinoma

Renal cell carcinoma (RCC) is a frequent urological malignancy characterized by a high rate of metastasis and lethality. The treatment strategy for advanced RCC has moved through multiple iterations over the past three decades. Initially, cytokine treatment was the only systemic treatment option for patients with RCC. With the development of medicine, antiangiogenic agents targeting vascular endothelial growth factor and mammalian target of rapamycin and immunotherapy, immune checkpoint inhibitors (ICIs) have emerged and received several achievements in the therapeutics of advanced RCC. However, ICIs have still not brought completely satisfactory results due to drug resistance and undesirable side effects. For the past years, the interests form researchers have been attracted by the combination of ICIs and targeted therapy for advanced RCC and the angiogenesis and immunogenic tumor microenvironmental variations in RCC. Therefore, we emphasize the potential principle and the clinical progress of ICIs combined with targeted treatment of advanced RCC, and summarize the future direction.

Dual-loss of PBRM1 and RAD51 identifies hyper-sensitive subset patients to immunotherapy in clear cell renal cell carcinoma

BACKGROUND

Homologous recombination deficiency (HRD), though largely uncharacterized in clear cell renal cell carcinoma (ccRCC), was found associated with RAD51 loss of expression. PBRM1 is the second most common mutated genes in ccRCC. Here, we introduce a HRD function-based PBRM1-RAD51 ccRCC classification endowed with diverse immune checkpoint blockade (ICB) responses.

METHODS

Totally 1542 patients from four independent cohorts were enrolled, including our localized Zhongshan hospital (ZSHS) cohort and Zhongshan hospital metastatic RCC (ZSHS-mRCC) cohort, The Cancer Genome Atlas (TCGA) cohort and CheckMate cohort. The genomic profile and immune microenvironment were depicted by genomic, transcriptome data and immunohistochemistry.

RESULTS

We observed that PBRM1-loss ccRCC harbored enriched HRD-associated mutational signature 3 and loss of RAD51. Dual-loss of PBRM1 and RAD51 identified patients hyper-sensitive to immunotherapy. This dual-loss subtype was featured by M1 macrophage infiltration. Dual-loss was, albeit homologous recombination defective, with high chromosomal stability.

CONCLUSIONS

PBRM1 and RAD51 dual-loss ccRCC indicates superior responses to immunotherapy. Dual-loss ccRCC harbors an immune-desert microenvironment but enriched with M1 macrophages. Dual-loss ccRCC is susceptible to defective homologous recombination but possesses high chromosomal stability.

Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment

The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.

Genomic ancestry in kidney cancer: Correlations with clinical and molecular features

INTRODUCTION

Genetic ancestry (GA) refers to population hereditary patterns that contribute to phenotypic differences seen among race/ethnicity groups, and differences among GA groups may highlight unique biological determinants that add to our understanding of health care disparities.

METHODS

A retrospective review of patients with renal cell carcinoma (RCC) was performed and correlated GA with clinicopathologic, somatic, and germline molecular data. All patients underwent next-generation sequencing of normal and tumor DNA using Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets, and contribution of African (AFR), East Asian (EAS), European (EUR), Native American, and South Asian (SAS) ancestry was inferred through supervised ADMIXTURE. Molecular data was compared across GA groups by Fisher exact test and Kruskal-Wallis test.

RESULTS

In 953 patients with RCC, the GA distribution was: EUR (78%), AFR (4.9%), EAS (2.5%), SAS (2%), Native American (0.2%), and Admixed (12.2%). GA distribution varied by tumor histology and international metastatic RCC database consortium disease risk status (intermediate-poor: EUR 58%, AFR 88%, EAS 74%, and SAS 73%). Pathogenic/likely pathogenic germline variants in cancer-predisposition genes varied (16% EUR, 23% AFR, 8% EAS, and 0% SAS), and most occurred in CHEK2 in EUR (3.1%) and FH in AFR (15.4%). In patients with clear cell RCC, somatic alteration incidence varied with significant enrichment in BAP1 alterations (EUR 17%, AFR 50%, SAS 29%; p = .01). Comparing AFR and EUR groups within The Cancer Genome Atlas, significant differences were identified in angiogenesis and inflammatory pathways.

CONCLUSION

Differences in clinical and molecular data by GA highlight population-specific variations in patients with RCC. Exploration of both genetic and nongenetic variables remains critical to optimize efforts to overcome health-related disparities.

Prognostic impact of lactylation-associated gene modifications in clear cell renal cell carcinoma: Insights into molecular landscape and therapeutic opportunities

Clear cell renal cell carcinoma (ccRCC) stands as a challenging subtype of kidney cancer, frequently complicating patient prognosis due to factors like postsurgical recurrences or late-stage diagnoses. In this study, we employed bioinformatics to investigate lactylation modifications in ccRCC, focusing on the TCGA-KIRC cohort. Out of 328 lactylation-associated genes, 31 emerged as differentially expressed, with 16 showing a marked correlation with overall survival. These genes exhibited strong protein-protein interactions and significant expression correlations. Intriguingly, a notable loss of gene copy numbers suggests potential implications in tumor progression. Utilizing unsupervised clustering, KIRC samples were grouped into two distinct subcategories, each showcasing different survival outcomes. While pathway enrichment highlighted an aggressive, inflammation-driven profile for subgroup 2, subgroup 1 was characterized by metabolic prominence. Furthermore, subgroup 2 presented an intensified inflammatory response, hinting at potential immune exhaustion. Capitalizing on machine learning, we developed a risk model using the TCGA-KIRC dataset, efficiently categorizing ccRCC patients into high- and low-risk clusters. Notably, those in the low-risk group indicated a more favorable survival trajectory. Clinical evaluations further corroborated these findings, linking better outcomes with reduced risk scores. Additionally, observed mutation patterns allude to a potential association between elevated risk scores and cytokine storms. TIDE analysis illuminated possible immunotherapeutic benefits for the low-risk group, underscored by an evident rise in microsatellite instability. Finally, our drug sensitivity evaluations revealed distinct therapeutic responses between the groups. In summary, this research underscores the pivotal role of lactylation modifications in ccRCC and introduces a promising prognostic model. These revelations pave the way for enhanced prognostic precision, presenting a promising path toward personalized treatment strategies and enriching our comprehension of the multifaceted molecular landscape of the disease.

B cell clonality in cancer

Carcinogenesis in the process of long-term co-evolution of tumor cells and immune environment essentially becomes possible due to incorrect decisions made, remembered, and reproduced by the immune system at the level of clonal populations of antigen-specific T- and B-lymphocytes. Tumor-immunity interaction determines the nature of such errors and, consequently, delineates the possible ways of successful immunotherapeutic intervention. It is generally recognized that tumor-infiltrating B cells (TIL-B) can play both pro-tumor and anti-tumor roles. However, the exact mechanisms that determine the contribution of clonal B cell lineages with different specificities and functions remain largely unclear. This is due to the variability of cancer types, the molecular heterogeneity of tumor cells, and, to a large extent, the individual pattern of each immune response. Further progress requires detailed investigation of the functional properties and phenotypes of clonally heterogeneous B cells in relation to their antigenic specificities, which determine the functionality of both effector B lymphocytes and immunoglobulins produced in the tumor environment. Based on a real understanding of the role of clonal antigen-specific populations of B lymphocytes in the tumor microenvironment, we need to learn how to develop new methods of targeted immunotherapy, as well as adapt existing treatment options to the specific needs of different patients and patient subgroups. In this review, we will cover B cells functional diversity and their multifaceted roles in the tumor environment.

Multi-omic profiling of clear cell renal cell carcinoma identifies metabolic reprogramming associated with disease progression

Clear cell renal cell carcinoma (ccRCC) is a complex disease with remarkable immune and metabolic heterogeneity. Here we perform genomic, transcriptomic, proteomic, metabolomic and spatial transcriptomic and metabolomic analyses on 100 patients with ccRCC from the Tongji Hospital RCC (TJ-RCC) cohort. Our analysis identifies four ccRCC subtypes including De-clear cell differentiated (DCCD)-ccRCC, a subtype with distinctive metabolic features. DCCD cancer cells are characterized by fewer lipid droplets, reduced metabolic activity, enhanced nutrient uptake capability and a high proliferation rate, leading to poor prognosis. Using single-cell and spatial trajectory analysis, we demonstrate that DCCD is a common mode of ccRCC progression. Even among stage I patients, DCCD is associated with worse outcomes and higher recurrence rate, suggesting that it cannot be cured by nephrectomy alone. Our study also suggests a treatment strategy based on subtype-specific immune cell infiltration that could guide the clinical management of ccRCC.

Understanding Factors that Influence Prognosis and Response to Therapy in Clear Cell Renal Cell Carcinoma

In this review, we highlight and contextualize emerging morphologic prognostic and predictive factors in renal cell carcinoma. We focus on clear cell renal cell carcinoma (ccRCC), the most common histologic subtype. Our understanding of the molecular characterization of ccRCC has dramatically improved in the last decade. Herein, we highlight how these discoveries have laid the foundation for new approaches to prognosis and therapeutic decision-making for patients with ccRCC. We explore the clinical relevance of common mutations, established gene expression signatures, intratumoral heterogeneity, sarcomatoid/rhabdoid morphology and PD-L1 expression, and discuss their impact on predicting response to therapy.

Comparative genomics incorporating translocation renal cell carcinoma mouse model reveals molecular mechanisms of tumorigenesis

Translocation renal cell carcinoma (tRCC) most commonly involves an ASPSCR1-TFE3 fusion, but molecular mechanisms remain elusive and animal models are lacking. Here, we show that human ASPSCR1-TFE3 driven by Pax8-Cre (a credentialed clear cell RCC driver) disrupted nephrogenesis and glomerular development, causing neonatal death, while the clear cell RCC failed driver, Sglt2-Cre, induced aggressive tRCC (as well as alveolar soft part sarcoma) with complete penetrance and short latency. However, in both contexts, ASPSCR1-TFE3 led to characteristic morphological cellular changes, loss of epithelial markers, and an epithelial-mesenchymal transition. Electron microscopy of tRCC tumors showed lysosome expansion, and functional studies revealed simultaneous activation of autophagy and mTORC1 pathways. Comparative genomic analyses encompassing an institutional human tRCC cohort (including a hitherto unreported SFPQ-TFEB fusion) and a variety of tumorgraft models (ASPSCR1-TFE3, PRCC-TFE3, SFPQ-TFE3, RBM10-TFE3, and MALAT1-TFEB) disclosed significant convergence in canonical pathways (cell cycle, lysosome, and mTORC1) and less established pathways such as Myc, E2F, and inflammation (IL-6/JAK/STAT3, interferon-γ, TLR signaling, systemic lupus, etc.). Therapeutic trials (adjusted for human drug exposures) showed antitumor activity of cabozantinib. Overall, this study provides insight into MiT/TFE-driven tumorigenesis, including the cell of origin, and characterizes diverse mouse models available for research.

Different outcomes among patients with intermediate-risk metastastic renal cell carcinoma treated with first-line tyrosine-kinase inhibitors

INTRODUCTION

Systemic therapy of patients with metastatic renal cell carcinoma (mRCC) has improved in the past years, with the advent of new immunotherapy-based combinations as a standard treatment option for first-line therapy. Nevertheless, particularly in good-risk patients by IMDC criteria, tyrosine-kinase inhibitors (TKI) may remain as an option for some patients. We reviewed our experience with TKI as first-line therapy for mRCC patients, trying to identify subgroups of patients that may still benefit from this strategy.

MATERIAL AND METHODS

All patients with mRCC treated with first-line TKI, and adequate follow-up, in University Hospital La Paz (Madrid, Spain) between 2007 and 2020 were analyzed. Patients treated inside a clinical trial were excluded from this analysis.

RESULTS

A total of 90 patients treated with first-line TKI were included. Regarding IMDC criteria, 33 patients (36.7%) were good-risk, 41 patients (45.5%) intermediate-risk, and 16 patients (17.8%) poor-risk. With a median follow-up of 49 months, the median overall survival (OS) for good, intermediate, and poor-risk patients was 54, 24, and 16 months (p = 0.004). When intermediate-risk was divided into patients with 1 or 2 risk factors, differences in OS were also statistically significant: patients with 1 risk factor had a median OS of 33 months, while patients with 2 risk factors had a median OS of 16 months, the same as poor-risk patients (p = 0.003). In the multivariate analysis, trying to find out which of the IMDC factors had a more remarkable weight in the prognosis of the patients, both ECOG and hemoglobin levels by themselves were significantly associated with OS.

CONCLUSION

In our group of patients, survival outcomes were different among patients with intermediate-risk with 1 or 2 risk factors by IMDC criteria. These could help select patients that may benefit from first-line treatment with a TKI, particularly in settings with difficult access to novel therapies, such as immunotherapy-based combinations.

Real-world Outcome of Patients with Advanced Renal Cell Carcinoma and Intermediate- or Poor-risk International Metastatic Renal Cell Carcinoma Database Consortium Criteria Treated by Immune-oncology Combinations: Differential Effectiveness by Risk Group?

BACKGROUND

Renal c carcinoma (RCC) is one of the most common urinary cancers worldwide, with a predicted increase in incidence in the coming years. Immunotherapy, as a single agent, in doublets, or in combination with anti-vascular endothelial growth factor receptor tyrosine kinase inhibitors (TKIs), has rapidly become a cornerstone of the RCC therapeutic scenario, but no head-to-head comparisons have been made. In this setting, real-world evidence emerges as a cornerstone to guide clinical decisions.

OBJECTIVE

The objective of this retrospective study was to assess the outcome of patients treated with first-line immune combinations or immune oncology (IO)-TKIs for advanced RCC.

DESIGN, SETTING, AND PARTICIPANTS

Data from 930 patients, 654 intermediate risk and 276 poor risk, were collected retrospectively from 58 centers in 20 countries. Special data such as sarcomatoid differentiation, body mass index, prior nephrectomy, and metastatic localization, in addition to biochemical data such as hemoglobin, platelets, calcium, lactate dehydrogenase, neutrophils, and radiological response by investigator's criteria, were collected.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan-Meier method. The median follow-up was calculated by the inverse Kaplan-Meier method.

RESULTS AND LIMITATIONS

The median follow-up time was 18.7 mo. In the 654 intermediate-risk patients, the median OS and PFS were significantly longer in patients with the intermediate than in those with the poor International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) criteria (38.9 vs 17.3 mo, 95% confidence interval [CI] p < 0.001, and 17.3 vs 11.6 mo, 95% CI p < 0.001, respectively). In the intermediate-risk subgroup, the OS was 55.7 mo (95% CI 31.4-55.7) and 40.2 mo (95% CI 29.6-51.6) in patients treated with IO + TKI and IO + IO combinations, respectively (p = 0.047). PFS was 30.7 mo (95% CI 16.5-55.7) and 13.2 mo (95% CI 29.6-51.6) in intermediate-risk patients treated with IO + TKI and IO + IO combinations, respectively (p < 0.001). In the poor-risk subgroup, the median OS and PFS did not show a statistically significant difference between IO + IO and IO + TKI. Our study presents several limitations, mainly due to its retrospective nature.

CONCLUSIONS

Our results showed differences between the IO + TKI and IO + IO combinations in intermediate-risk patients. A clear association with longer PFS and OS in favor of patients who received the IO + TKI combinations compared with the IO-IO combination was observed. Instead, in the poor-risk group, we observed no significant difference in PFS or OS between patients who received different combinations.

PATIENT SUMMARY

Renal cancer is one of the most frequent genitourinary tumors. Treatment is currently based on immunotherapy combinations or immunotherapy with tyrosine kinase inhibitors, but there are no comparisons between these.In this study, we have analyzed the clinical course of 930 patients from 58 centers in 20 countries around the world. We aimed to analyze the differences between the two main treatment strategies, combination of two immunotherapies versus immunotherapy + antiangiogenic therapy, and found in real-life data that intermediate-risk patients (approximately 60% of patients with metastatic renal cancer) seem to benefit more from the combination of immunotherapy + antiangiogenic therapy than from double immunotherapy. No such differences were found in poor-risk patients. This may have important implications in daily practice decision-making for these patients.

MAEL in human cancers and implications in prognostication and predicting benefit from immunotherapy over VEGFR/mTOR inhibitors in clear cell renal cell carcinoma: a bioinformatic analysis

Maelstrom (MAEL), a novel cancer/testis-associated gene, may facilitate the initiation and progression of human malignancies, warranting comprehensive investigations. Single-cell and tissue-bulk transcriptomic data demonstrated higher MAEL expression in testis (spermatogonia/spermatocyte), kidney (proximal tubular cell), and brain (neuron/astrocyte), and corresponding cancers, including testicular germ cell tumor, glioma, papillary renal cell carcinoma, and clear cell renal cell carcinoma (ccRCC). Of these cancers, only in ccRCC did MAEL expression exhibit associations with both recurrence-free survival and overall survival. High MAEL expression was associated with an anti-inflammatory tumor immune microenvironment and VEGFR/mTOR activation in ccRCC tissues and high sensitivities to VEGFR/PI3K-AKT-mTOR inhibitors in ccRCC cell lines. Consistent with these, low rather than high MAEL expression indicated remarkable progression-free survival benefits from immune checkpoint inhibitor (ICI)-based immunotherapies over VEGFR/mTOR inhibitors in two large phase III trials (JAVELIN Renal 101 and CheckMate-025). MAEL is a biologically and clinically significant determinant with potential for prognostication after nephrectomy and patient selection for VEGFR/mTOR inhibitors and immunotherapy-based treatments.

Updated systematic review and network meta-analysis of first-line treatments for metastatic renal cell carcinoma with extended follow-up data

Immune checkpoint inhibitor (ICI)-based combination therapies are the recommended first-line treatment for metastatic renal cell carcinoma (mRCC). However, no head-to-head phase-3 randomized controlled trials (RCTs) have compared the efficacy of different ICI-based combination therapies. Here, we compared the efficacy of various first-line ICI-based combination therapies in patients with mRCC using updated survival data from phase-3 RCTs. Three databases were searched in June 2023 for RCTs that analyzed oncologic outcomes in mRCC patients treated with ICI-based combination therapies as first-line treatment. A network meta-analysis compared outcomes including overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and complete response (CR) rate. Subgroup analyses were based on the International mRCC Database Consortium risk classification. The treatment ranking analysis of the entire cohort showed that nivolumab + cabozantinib (81%) had the highest likelihood of improving OS, followed by nivolumab + ipilimumab (75%); pembrolizumab + lenvatinib had the highest likelihood of improving PFS (99%), ORR (97%), and CR (86%). These results remained valid even when the analysis was limited to patients with intermediate/poor risk, except that nivolumab + ipilimumab had the highest likelihood of achieving CR (100%). Further, OS benefits of ICI doublets were not inferior to those of ICI + tyrosine kinase inhibitor combinations. Recommendation of combination therapies with ICIs and/or tyrosine kinase inhibitors based on survival benefits and patient pretreatment risk classification will help advance personalized medicine for mRCC.

The Role of the Complement in Clear Cell Renal Carcinoma (ccRCC)-What Future Prospects Are There for Its Use in Clinical Practice?

In recent years, the first-line available therapeutic options for metastatic renal cell carcinoma (mRCC) have radically changed with the introduction into clinical practice of new immune checkpoint inhibitor (ICI)-based combinations. Many efforts are focusing on identifying novel prognostic and predictive markers in this setting. The complement system (CS) plays a central role in promoting the growth and progression of mRCC. In particular, mRCC has been defined as an "aggressive complement tumor", which encompasses a group of malignancies with poor prognosie and highly expressed complement components. Several preclinical and retrospective studies have demonstrated the negative prognostic role of the complement in mRCC; however, there is little evidence on its possible role as a predictor of the response to ICIs. The purpose of this review is to explore more deeply the physio-pathological role of the complement in the development of RCC and its possible future use in clinical practice as a prognostic and predictive factor.

Notch activation defines immune-suppressive subsets of ccRCCs with unfavorable benefits from immunotherapy over VEGFR/mTOR inhibitors

The evolutionarily conserved Notch pathway, involved in cancer stem cell capacity and cancer immunity, may predict the benefit from immune checkpoint inhibitors (ICIs) in clear cell renal cell carcinoma (ccRCC). In the TCGA dataset, mRNA expression of Notch pathway genes identified three clusters with different prognoses and molecular characteristics. Based on the differentially expressed Notch pathway genes between clusters, we constructed the Notch-score, correlated with Notch activation, angiogenesis, PI3K-AKT-mTOR activity, and sensitivities to VEGFR/mTOR inhibitors. A high Notch-score was linked with more "resting"/"anti-inflammatory" rather than "activated"/"pro-inflammatory" tumor-infiltrating immune cells, inactivated immune pathways, and scarce any benefits from ICI-based therapies over VEGFR/mTOR inhibitors in the JAVELIN Renal 101 (avelumab plus axitinib vs. sunitinib) and the CheckMate-009/010/025 trials (nivolumab vs. everolimus). For the Notch-activated ccRCCs, ICIs provide limited advantages and might not be strongly recommended, by which the cost-effectiveness of treatments in ccRCCs may be potentially improved.

Immunomodulatory response to neoadjuvant nivolumab in non-metastatic clear cell renal cell carcinoma

Novel perioperative strategies are needed to reduce recurrence rates in patients undergoing nephrectomy for high-risk, non-metastatic clear cell renal cell carcinoma (ccRCC). We conducted a prospective, phase I trial of neoadjuvant nivolumab prior to nephrectomy in 15 evaluable patients with non-metastatic ccRCC. We leveraged tissue from that cohort to elucidate the effects of PD-1 inhibition on immune cell populations in ccRCC and correlate the evolving immune milieu with anti-PD-1 response. We found that nivolumab durably induces a pro-inflammatory state within the primary tumor, and baseline immune infiltration within the primary tumor correlates with nivolumab responsiveness. Nivolumab increases CTLA-4 expression in the primary tumor, and subsequent nephrectomy increases circulating concentrations of sPD-L1, sPD-L3 (sB7-H3), and s4-1BB. These findings form the basis to consider neoadjuvant immune checkpoint inhibition (ICI) for high-risk ccRCC while the tumor remains in situ and provide the rationale for perioperative strategies of novel ICI combinations.

TCCIA: a comprehensive resource for exploring CircRNA in cancer immunotherapy

BACKGROUND

Immunotherapies targeting immune checkpoints have gained increasing attention in cancer treatment, emphasizing the need for predictive biomarkers. Circular RNAs (circRNAs) have emerged as critical regulators of tumor immunity, particularly in the PD-1/PD-L1 pathway, and have shown potential in predicting immunotherapy efficacy. Yet, the detailed roles of circRNAs in cancer immunotherapy are not fully understood. While existing databases focus on either circRNA profiles or immunotherapy cohorts, there is currently no platform that enables the exploration of the intricate interplay between circRNAs and anti-tumor immunotherapy. A comprehensive resource combining circRNA profiles, immunotherapy responses, and clinical outcomes is essential to advance our understanding of circRNA-mediated tumor-immune interactions and to develop effective biomarkers.

METHODS

To address these gaps, we constructed The Cancer CircRNA Immunome Atlas (TCCIA), the first database that combines circRNA profiles, immunotherapy response data, and clinical outcomes across multicancer types. The construction of TCCIA involved applying standardized preprocessing to the raw sequencing FASTQ files, characterizing circRNA profiles using an ensemble approach based on four established circRNA detection tools, analyzing tumor immunophenotypes, and compiling immunotherapy response data from diverse cohorts treated with immune checkpoint blockades (ICBs).

RESULTS

TCCIA encompasses over 4,000 clinical samples obtained from 25 cohorts treated with ICBs along with other treatment modalities. The database provides researchers and clinicians with a cloud-based platform that enables interactive exploration of circRNA data in the context of ICB. The platform offers a range of analytical tools, including browse of identified circRNAs, visualization of circRNA abundance and correlation, association analysis between circRNAs and clinical variables, assessment of the tumor immune microenvironment, exploration of tumor molecular signatures, evaluation of treatment response or prognosis, and identification of altered circRNAs in immunotherapy-sensitive and resistant tumors. To illustrate the utility of TCCIA, we showcase two examples, including circTMTC3 and circMGA, by employing analysis of large-scale melanoma and bladder cancer cohorts, which unveil distinct impacts and clinical implications of different circRNA expression in cancer immunotherapy.

CONCLUSIONS

TCCIA represents a significant advancement over existing resources, providing a comprehensive platform to investigate the role of circRNAs in immuno-oncology.

Update on Biomarkers in Renal Cell Carcinoma

Immune checkpoint inhibitors have significantly transformed the treatment paradigm for metastatic renal cell carcinoma (RCC), offering prolonged overall survival and achieving remarkable deep and durable responses. However, given the multiple ICI-containing, standard-of-care regimens approved for RCC, identifying biomarkers that predict therapeutic response and resistance is of critical importance. Although tumor-intrinsic features such as pathological characteristics, genomic alterations, and transcriptional signatures have been extensively investigated, they have yet to provide definitive, robust predictive biomarkers. Current research is exploring host factors through in-depth characterization of the immune system. Additionally, innovative technological approaches are being developed to overcome challenges presented by existing techniques, such as tumor heterogeneity. Promising avenues in biomarker discovery include the study of the microbiome, radiomics, and spatial transcriptomics.